Learning Bayes Nets for Relational Data and Heterogenous Networks

学习关系数据和异构网络的贝叶斯网络

• 批准号: 217331-2013
• 负责人: Schulte, Oliver
• 金额: $ 1.46万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=593974
• 关键词: Learning; Bayes; Nets; Relational; Data

Vast amounts of data are gathered daily for different purposes. Many, if not most, new datasets contain information about networks of linked entities. Sources of link data include social media, such as Twitter, social networks, computer networks such as the Internet, biological networks, and the relational databases that most organizations use to maintain their data. A crucial competitive edge comes with the ability to efficiently find informative patterns in network data and to learn from them to improve decision-making under uncertainty. The aim of this project is to extend and enhance computational tools from Machine Learning and Artificial Intelligence so that they can be applied to network data. Key statistical questions include the following.

每天都有大量的数据被收集用于不同的目的。许多（如果不是大多数）新数据集包含有关链接实体网络的信息。链接数据的来源包括社交媒体（如Twitter）、社交网络、计算机网络（如互联网）、生物网络以及大多数组织用于维护其数据的关系数据库。一个关键的竞争优势是能够有效地发现网络数据中的信息模式，并从中学习，以改善不确定性下的决策。该项目的目的是扩展和增强机器学习和人工智能的计算工具，以便它们可以应用于网络数据。关键的统计问题包括以下方面。